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Exploring the depths of the global earth observation system of systems

机译:探索全球地球观测系统的系统深度

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This paper explores for the first time the contents, structure and relationships across institutions and disciplines of a global Big Earth Data cyber-infrastructure: the Global Earth Observation System of System (GEOSS). The analysis builds on 1.8 million metadata records harvested in GEOSS. Because this set includes almost all the major large data collections in GEOSS, the analysis represents more than 80% of all the data made available through this global system. We explore two major aspects: the collaborative networks and the thematic coverage in GEOSS. The first connects the contributing organisations through the more than 200,000 keywords used in the systems, and then explores who is citing whom, a proxy for of institutional thickness. The thematic coverage is analysed through neural network algorithms, first on the keywords, and then on the corpus of 653 million lemmatised lower case words built from the titles and abstracts of all 1.8 million metadata records. The findings not only give a good overview of the GEOSS data universe, but offer immediate priorities on how to increase the usability of GEOSS through improved data management, and the opportunity to augment the metadata with high level concept that synthetise well the contents of the data-set.
机译:本文首次探讨了全球大地球数据网络基础设施的各个机构和学科的内容,结构和关系:全球地球观测系统系统(GEOSS)。该分析基于在GEOSS中收集的180万个元数据记录。由于该集合几乎包括GEOSS中的所有主要大数据集合,因此该分析代表了该全球系统提供的所有数据的80%以上。我们探索两个主要方面:GEOSS中的协作网络和主题范围。首先,它通过系统中使用的200,000多个关键字将参与组织联系起来,然后研究谁在引用谁,这是机构厚度的代表。通过神经网络算法分析主题覆盖范围,首先是关键词,然后是从全部180万个元数据记录的标题和摘要构建的6.53亿个经过修饰的小写单词的语料库。这些发现不仅提供了对GEOSS数据世界的良好概述,而且为如何通过改进的数据管理来提高GEOSS的可用性提供了当务之急,并提供了利用高级概念来扩充元数据的机会,这些概念可以很好地综合数据的内容-组。

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